DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.

Background: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data.

Dietary Supplementation with Pomegranate and Onion Affects Lipid and Protein Oxidation in the Breast Meat, Thigh, and Liver, Cellular Stress Protein Responses, and Gene Expression of Liver Enzymes Involved in Protein Synthesis in Broilers.

The present study examined the effects of dietary supplementation with extracts of pomegranate () and onion (), either encapsulated in cyclodextrin (POMALCD group) or in an aqueous (POMALAQ group) form, on breast meat, thigh meat, and liver composition, oxidative stability, cellular signaling pathways, and the gene expression of certain hepatic genes. The results showed that breast and thigh meat contained significantly ( < 0.05) higher moisture content in the group with the aqueous extract, compared to the control and POMALCD groups.

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are crucial to help understand the network of relationships between the predictor and response variables, as well as the conditional relationships among the latter. While Bayesian methods have the advantage of providing natural uncertainty quantification through posterior inclusion probabilities and credible intervals, current Bayesian approaches either restrict to specific sub-classes of sparsity patterns and/or are not scalable to settings with hundreds of responses and predictors.

Serum lipidomic determinants of human diabetic neuropathy in type 2 diabetes.

Objective: The serum lipidomic profile associated with neuropathy in type 2 diabetes is not well understood. Obesity and dyslipidemia are known neuropathy risk factors, suggesting lipid profiles early during type 2 diabetes may identify individuals who develop neuropathy later in the disease course. This retrospective cohort study examined lipidomic profiles 10 years prior to type 2 diabetic neuropathy assessment.

Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease.

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart.

Toughening and Healing of CFRPs by Diels-Alder-Based Nano-Modified Resin through Melt Electro-Writing Process Technique.

In the current study, a novel approach in terms of the incorporation of self-healing agent (SHA) into unidirectional (UD) carbon fiber reinforced plastics (CFRPs) has been demonstrated. More precisely, Diels-Alder (DA) mechanism-based resin (Bis-maleimide type) containing or not four layered graphene nanoplatelets (GNPs) at the amount of 1 wt% was integrated locally in the mid-thickness area of CFRPs by melt electro-writing process (MEP). Based on that, CFRPs containing or not SHA were fabricated and further tested under Mode I interlaminar fracture toughness experiments.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis.

Lipid Alterations in African American Men with Prostate Cancer.

African-American (AA) men are more than twice as likely to die of prostate cancer (PCa) than European American (EA) men. Previous in silico analysis revealed enrichment of altered lipid metabolic pathways in pan-cancer AA tumors. Here, we performed global unbiased lipidomics profiling on 48 matched localized PCa and benign adjacent tissues (30 AA, 24 ancestry-verified, and 18 EA, 8 ancestry verified) and quantified 429 lipids belonging to 14 lipid classes.

A Fast Detection Method of Break Points in Effective Connectivity Networks.

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neurogimaging data, based on vector autoregressive (Granger causal) models.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease.

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures.

Diminished retinal complex lipid synthesis and impaired fatty acid β-oxidation associated with human diabetic retinopathy.

BACKGROUNDThis study systematically investigated circulating and retinal tissue lipid determinants of human diabetic retinopathy (DR) to identify underlying lipid alterations associated with severity of DR.METHODSRetinal tissues were retrieved from postmortem human eyes, including 19 individuals without diabetes, 20 with diabetes but without DR, and 20 with diabetes and DR, for lipidomic study. In a parallel study, serum samples from 28 American Indians with type 2 diabetes from the Gila River Indian Community, including 12 without DR, 7 with mild nonproliferative DR (NPDR), and 9 with moderate NPDR, were selected.

Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating -Regulatory Quantitative Effects.

Background: The development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples.

Circulating Free Fatty Acid and Phospholipid Signature Predicts Early Rapid Kidney Function Decline in Patients With Type 1 Diabetes.

Objectives: Patients with type 1 diabetes (T1D) exhibit modest lipid abnormalities as measured by traditional metrics. This study aimed to identify lipidomic predictors of rapid decline of kidney function in T1D.

Research Design And Methods: In a case-control study, 817 patients with T1D from three large cohorts were randomly split into training and validation subsets.

Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease.

Objective: Dyslipidemia is a significant risk factor for progression of diabetic kidney disease (DKD). Determining the changes in individual lipids and lipid networks across a spectrum of DKD severity may identify lipids that are pathogenic to DKD progression.

Methods: We performed untargeted lipidomic analysis of kidney cortex tissue from diabetic db/db and db/db eNOS mice along with non-diabetic littermate controls.

County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States.

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify "vulnerable" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers.

Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations.

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state.

Plasma lipidomic profiling identifies a novel complex lipid signature associated with ischemic stroke in chronic kidney disease.

Rationale And Objective: Despite contribution of dyslipidemia to ischemic stroke, plasma lipidomic correlates of stroke in CKD is not studied. This study is aimed to identify plasma lipid alterations associated with stroke.

Study Design: Cross sectional.

Modeling and Optimization of Time-Of-Use Electricity Pricing Systems.

Time-of-Use (TOU) pricing is an important strategy for electricity providers to manage supply and hence making the grid more efficient and for consumers to manage their costs. In this paper, we discuss a general stochastic modeling framework for consumer's power demand based on which the TOU contract characteristics can be selected, so as to minimize the mean electricity price paid by the customer. We exploit the characteristics of power demand observed in real grids to propose to model it during homogeneous peak periods as a constant level with fluctuations described by a scaled fractional Brownian motion.

Gut microbiota dysbiosis and altered tryptophan catabolism contribute to autoimmunity in lupus-susceptible mice.

The autoimmune disease systemic lupus erythematosus (SLE) is characterized by the production of pathogenic autoantibodies. It has been postulated that gut microbial dysbiosis may be one of the mechanisms involved in SLE pathogenesis. Here, we demonstrate that the dysbiotic gut microbiota of triple congenic (TC) lupus-prone mice (B6.

Synergistic, bacteriostatic effect of propolis and glycerol against in chocolate milk under refrigerated storage.

Propolis ethanolic extracts, with or without glycerol, were added into pasteurized, non-fat chocolate milk, which was artificially contaminated with . The addition of propolis ethanolic extracts dissolved into glycerol led to a definite anti-listerial effect in milk stored at 4 ℃, with both propolis concentrations tested (2 or 4 mg of dry propolis ethanolic extract per milliliter of chocolate milk) leading to inhibition of growth throughout 20 days of storage. The combined addition of propolis ethanolic extracts with glycerol was also effective in significantly reducing the rate of growth of in chocolate milk stored under improper (10 ℃) refrigeration storage conditions (more than five-fold increase in the generation time of compared to control trials).